Analytica represents and computes each uncertain quantity as a random sample from its probability distribution. You can select several uncertainty views including Probability bands, Density function, or Cumulative probability distribution. The accuracy depends on the number of samples. The default sample size of 1000 is usually fine for initial tests as you build a model. But, it may result in some noise, especially in the probability density, which is much more sensitive to random variation than the cumulative probability view. A simple way to reduce the noise is to set the Smoothing in the Probability density tab of the Uncertainty Setup dialog. We recommend the default smoothing factor. Higher levels can be misleading, e.g., with tails too wide.
Sometimes you need a larger sample size. How large? Some analysts do extensive “convergence tests” rerunning their model with various sample sizes. That’s usually a waste of time. For Monte Carlo simulation, you can figure out what sample size you need using simple statistics. Suppose you care about the mean (expected value) of a result, and want to be 95% confident that the true mean is within +-0.5% of the estimated mean. You can read the needed sample size from this graph – about 60,000. Or you can specify a desired interval on, say, 95% percentile. See Selecting the sample size for more. It includes the Choose_sample_size.ANA library which you can download with a click to let you calculate the sample size you need.